Overview

Dataset statistics

Number of variables13
Number of observations43824
Missing cells2067
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory104.0 B

Variable types

Numeric11
Categorical2

Alerts

pm2.5 has 2067 (4.7%) missing valuesMissing
No is uniformly distributedUniform
year is uniformly distributedUniform
No has unique valuesUnique
hour has 1826 (4.2%) zerosZeros
DEWP has 831 (1.9%) zerosZeros
TEMP has 1133 (2.6%) zerosZeros
Is has 43456 (99.2%) zerosZeros
Ir has 42016 (95.9%) zerosZeros

Reproduction

Analysis started2023-12-06 23:02:24.759124
Analysis finished2023-12-06 23:02:37.063661
Duration12.3 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

No
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct43824
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21912.5
Minimum1
Maximum43824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2023-12-07T06:02:37.150237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2192.15
Q110956.75
median21912.5
Q332868.25
95-th percentile41632.85
Maximum43824
Range43823
Interquartile range (IQR)21911.5

Descriptive statistics

Standard deviation12651.043
Coefficient of variation (CV)0.57734368
Kurtosis-1.2
Mean21912.5
Median Absolute Deviation (MAD)10956
Skewness0
Sum9.602934 × 108
Variance1.600489 × 108
MonotonicityStrictly increasing
2023-12-07T06:02:37.268813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
29220 1
 
< 0.1%
29212 1
 
< 0.1%
29213 1
 
< 0.1%
29214 1
 
< 0.1%
29215 1
 
< 0.1%
29216 1
 
< 0.1%
29217 1
 
< 0.1%
29218 1
 
< 0.1%
29219 1
 
< 0.1%
Other values (43814) 43814
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
43824 1
< 0.1%
43823 1
< 0.1%
43822 1
< 0.1%
43821 1
< 0.1%
43820 1
< 0.1%
43819 1
< 0.1%
43818 1
< 0.1%
43817 1
< 0.1%
43816 1
< 0.1%
43815 1
< 0.1%

year
Categorical

UNIFORM 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size342.5 KiB
2012
8784 
2010
8760 
2011
8760 
2013
8760 
2014
8760 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters175296
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010
2nd row2010
3rd row2010
4th row2010
5th row2010

Common Values

ValueCountFrequency (%)
2012 8784
20.0%
2010 8760
20.0%
2011 8760
20.0%
2013 8760
20.0%
2014 8760
20.0%

Length

2023-12-07T06:02:37.374379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-07T06:02:37.477349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2012 8784
20.0%
2010 8760
20.0%
2011 8760
20.0%
2013 8760
20.0%
2014 8760
20.0%

Most occurring characters

ValueCountFrequency (%)
2 52608
30.0%
0 52584
30.0%
1 52584
30.0%
3 8760
 
5.0%
4 8760
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 175296
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 52608
30.0%
0 52584
30.0%
1 52584
30.0%
3 8760
 
5.0%
4 8760
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 175296
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 52608
30.0%
0 52584
30.0%
1 52584
30.0%
3 8760
 
5.0%
4 8760
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 175296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 52608
30.0%
0 52584
30.0%
1 52584
30.0%
3 8760
 
5.0%
4 8760
 
5.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5235487
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2023-12-07T06:02:37.578063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4485725
Coefficient of variation (CV)0.52863443
Kurtosis-1.2078571
Mean6.5235487
Median Absolute Deviation (MAD)3
Skewness-0.0095266123
Sum285888
Variance11.892652
MonotonicityNot monotonic
2023-12-07T06:02:37.677620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 3720
8.5%
3 3720
8.5%
5 3720
8.5%
7 3720
8.5%
8 3720
8.5%
10 3720
8.5%
12 3720
8.5%
4 3600
8.2%
6 3600
8.2%
9 3600
8.2%
Other values (2) 6984
15.9%
ValueCountFrequency (%)
1 3720
8.5%
2 3384
7.7%
3 3720
8.5%
4 3600
8.2%
5 3720
8.5%
6 3600
8.2%
7 3720
8.5%
8 3720
8.5%
9 3600
8.2%
10 3720
8.5%
ValueCountFrequency (%)
12 3720
8.5%
11 3600
8.2%
10 3720
8.5%
9 3600
8.2%
8 3720
8.5%
7 3720
8.5%
6 3600
8.2%
5 3720
8.5%
4 3600
8.2%
3 3720
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.72782
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2023-12-07T06:02:37.777197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7994247
Coefficient of variation (CV)0.55948151
Kurtosis-1.1938547
Mean15.72782
Median Absolute Deviation (MAD)8
Skewness0.0069122681
Sum689256
Variance77.429876
MonotonicityNot monotonic
2023-12-07T06:02:37.891067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1440
 
3.3%
2 1440
 
3.3%
28 1440
 
3.3%
27 1440
 
3.3%
26 1440
 
3.3%
25 1440
 
3.3%
24 1440
 
3.3%
23 1440
 
3.3%
22 1440
 
3.3%
21 1440
 
3.3%
Other values (21) 29424
67.1%
ValueCountFrequency (%)
1 1440
3.3%
2 1440
3.3%
3 1440
3.3%
4 1440
3.3%
5 1440
3.3%
6 1440
3.3%
7 1440
3.3%
8 1440
3.3%
9 1440
3.3%
10 1440
3.3%
ValueCountFrequency (%)
31 840
1.9%
30 1320
3.0%
29 1344
3.1%
28 1440
3.3%
27 1440
3.3%
26 1440
3.3%
25 1440
3.3%
24 1440
3.3%
23 1440
3.3%
22 1440
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1826
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2023-12-07T06:02:37.999590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222655
Coefficient of variation (CV)0.60193613
Kurtosis-1.2041744
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum503976
Variance47.91776
MonotonicityNot monotonic
2023-12-07T06:02:38.105670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1826
 
4.2%
1 1826
 
4.2%
22 1826
 
4.2%
21 1826
 
4.2%
20 1826
 
4.2%
19 1826
 
4.2%
18 1826
 
4.2%
17 1826
 
4.2%
16 1826
 
4.2%
15 1826
 
4.2%
Other values (14) 25564
58.3%
ValueCountFrequency (%)
0 1826
4.2%
1 1826
4.2%
2 1826
4.2%
3 1826
4.2%
4 1826
4.2%
5 1826
4.2%
6 1826
4.2%
7 1826
4.2%
8 1826
4.2%
9 1826
4.2%
ValueCountFrequency (%)
23 1826
4.2%
22 1826
4.2%
21 1826
4.2%
20 1826
4.2%
19 1826
4.2%
18 1826
4.2%
17 1826
4.2%
16 1826
4.2%
15 1826
4.2%
14 1826
4.2%

pm2.5
Real number (ℝ)

MISSING 

Distinct581
Distinct (%)1.4%
Missing2067
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean98.613215
Minimum0
Maximum994
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2023-12-07T06:02:38.242260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q129
median72
Q3137
95-th percentile284
Maximum994
Range994
Interquartile range (IQR)108

Descriptive statistics

Standard deviation92.050387
Coefficient of variation (CV)0.9334488
Kurtosis4.7689333
Mean98.613215
Median Absolute Deviation (MAD)49
Skewness1.8023114
Sum4117792
Variance8473.2738
MonotonicityNot monotonic
2023-12-07T06:02:38.535119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 626
 
1.4%
11 596
 
1.4%
13 589
 
1.3%
12 578
 
1.3%
17 572
 
1.3%
10 523
 
1.2%
9 509
 
1.2%
14 491
 
1.1%
15 486
 
1.1%
18 480
 
1.1%
Other values (571) 36307
82.8%
(Missing) 2067
 
4.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 5
 
< 0.1%
2 24
 
0.1%
3 49
 
0.1%
4 75
 
0.2%
5 109
 
0.2%
6 239
0.5%
7 296
0.7%
8 382
0.9%
9 509
1.2%
ValueCountFrequency (%)
994 1
< 0.1%
980 1
< 0.1%
972 1
< 0.1%
886 1
< 0.1%
858 1
< 0.1%
852 1
< 0.1%
845 1
< 0.1%
824 1
< 0.1%
810 1
< 0.1%
805 1
< 0.1%

DEWP
Real number (ℝ)

ZEROS 

Distinct69
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8172463
Minimum-40
Maximum28
Zeros831
Zeros (%)1.9%
Negative20006
Negative (%)45.7%
Memory size342.5 KiB
2023-12-07T06:02:38.662093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-40
5-th percentile-21
Q1-10
median2
Q315
95-th percentile22
Maximum28
Range68
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.43344
Coefficient of variation (CV)7.942479
Kurtosis-1.195289
Mean1.8172463
Median Absolute Deviation (MAD)13
Skewness-0.15244742
Sum79639
Variance208.3242
MonotonicityNot monotonic
2023-12-07T06:02:38.796530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 1323
 
3.0%
17 1294
 
3.0%
19 1290
 
2.9%
16 1238
 
2.8%
20 1219
 
2.8%
15 1162
 
2.7%
21 1159
 
2.6%
22 1145
 
2.6%
-5 1095
 
2.5%
12 1076
 
2.5%
Other values (59) 31823
72.6%
ValueCountFrequency (%)
-40 1
 
< 0.1%
-39 1
 
< 0.1%
-38 2
 
< 0.1%
-37 3
 
< 0.1%
-36 3
 
< 0.1%
-35 4
 
< 0.1%
-34 6
 
< 0.1%
-33 14
< 0.1%
-32 17
< 0.1%
-31 8
< 0.1%
ValueCountFrequency (%)
28 11
 
< 0.1%
27 36
 
0.1%
26 110
 
0.3%
25 269
 
0.6%
24 616
1.4%
23 904
2.1%
22 1145
2.6%
21 1159
2.6%
20 1219
2.8%
19 1290
2.9%

TEMP
Real number (ℝ)

ZEROS 

Distinct64
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.448521
Minimum-19
Maximum42
Zeros1133
Zeros (%)2.6%
Negative8614
Negative (%)19.7%
Memory size342.5 KiB
2023-12-07T06:02:38.938601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile-7
Q12
median14
Q323
95-th percentile30
Maximum42
Range61
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.198613
Coefficient of variation (CV)0.97992464
Kurtosis-1.1109767
Mean12.448521
Median Absolute Deviation (MAD)10
Skewness-0.16330363
Sum545544
Variance148.80615
MonotonicityNot monotonic
2023-12-07T06:02:39.065188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 1566
 
3.6%
23 1538
 
3.5%
22 1433
 
3.3%
21 1400
 
3.2%
25 1397
 
3.2%
20 1364
 
3.1%
26 1297
 
3.0%
19 1229
 
2.8%
-1 1200
 
2.7%
27 1181
 
2.7%
Other values (54) 30219
69.0%
ValueCountFrequency (%)
-19 2
 
< 0.1%
-18 7
 
< 0.1%
-17 12
 
< 0.1%
-16 24
 
0.1%
-15 43
 
0.1%
-14 68
 
0.2%
-13 110
 
0.3%
-12 176
0.4%
-11 261
0.6%
-10 304
0.7%
ValueCountFrequency (%)
42 1
 
< 0.1%
41 5
 
< 0.1%
40 7
 
< 0.1%
39 4
 
< 0.1%
38 12
 
< 0.1%
37 34
 
0.1%
36 60
 
0.1%
35 139
0.3%
34 246
0.6%
33 324
0.7%

PRES
Real number (ℝ)

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1016.4477
Minimum991
Maximum1046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2023-12-07T06:02:39.197743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum991
5-th percentile1001
Q11008
median1016
Q31025
95-th percentile1033
Maximum1046
Range55
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.268698
Coefficient of variation (CV)0.010102535
Kurtosis-0.84646214
Mean1016.4477
Median Absolute Deviation (MAD)8
Skewness0.098206838
Sum44544802
Variance105.44616
MonotonicityNot monotonic
2023-12-07T06:02:39.328861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014 1504
 
3.4%
1006 1445
 
3.3%
1013 1443
 
3.3%
1012 1382
 
3.2%
1025 1375
 
3.1%
1015 1374
 
3.1%
1023 1360
 
3.1%
1007 1351
 
3.1%
1017 1346
 
3.1%
1018 1311
 
3.0%
Other values (50) 29933
68.3%
ValueCountFrequency (%)
991 2
 
< 0.1%
992 4
 
< 0.1%
993 18
 
< 0.1%
994 64
 
0.1%
995 106
 
0.2%
996 153
 
0.3%
997 246
0.6%
998 422
1.0%
999 407
0.9%
1000 597
1.4%
ValueCountFrequency (%)
1046 11
 
< 0.1%
1045 5
 
< 0.1%
1044 5
 
< 0.1%
1043 7
 
< 0.1%
1042 23
 
0.1%
1041 74
 
0.2%
1040 114
0.3%
1039 128
0.3%
1038 175
0.4%
1037 249
0.6%

cbwd
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size342.5 KiB
SE
15290 
NW
14150 
cv
9387 
NE
4997 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters87648
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowNW
3rd rowNW
4th rowNW
5th rowNW

Common Values

ValueCountFrequency (%)
SE 15290
34.9%
NW 14150
32.3%
cv 9387
21.4%
NE 4997
 
11.4%

Length

2023-12-07T06:02:39.440449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-07T06:02:39.535013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
se 15290
34.9%
nw 14150
32.3%
cv 9387
21.4%
ne 4997
 
11.4%

Most occurring characters

ValueCountFrequency (%)
E 20287
23.1%
N 19147
21.8%
S 15290
17.4%
W 14150
16.1%
c 9387
10.7%
v 9387
10.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 68874
78.6%
Lowercase Letter 18774
 
21.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 20287
29.5%
N 19147
27.8%
S 15290
22.2%
W 14150
20.5%
Lowercase Letter
ValueCountFrequency (%)
c 9387
50.0%
v 9387
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87648
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 20287
23.1%
N 19147
21.8%
S 15290
17.4%
W 14150
16.1%
c 9387
10.7%
v 9387
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 20287
23.1%
N 19147
21.8%
S 15290
17.4%
W 14150
16.1%
c 9387
10.7%
v 9387
10.7%

Iws
Real number (ℝ)

Distinct2788
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.88914
Minimum0.45
Maximum585.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2023-12-07T06:02:39.652573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile0.89
Q11.79
median5.37
Q321.91
95-th percentile113.99
Maximum585.6
Range585.15
Interquartile range (IQR)20.12

Descriptive statistics

Standard deviation50.010635
Coefficient of variation (CV)2.0934465
Kurtosis23.421309
Mean23.88914
Median Absolute Deviation (MAD)4.48
Skewness4.2978927
Sum1046917.7
Variance2501.0636
MonotonicityNot monotonic
2023-12-07T06:02:39.799114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.89 6266
 
14.3%
1.79 4807
 
11.0%
3.13 1932
 
4.4%
1.78 1836
 
4.2%
4.92 1251
 
2.9%
3.58 1197
 
2.7%
4.02 863
 
2.0%
2.68 712
 
1.6%
2.67 680
 
1.6%
7.15 542
 
1.2%
Other values (2778) 23738
54.2%
ValueCountFrequency (%)
0.45 463
 
1.1%
0.89 6266
14.3%
0.9 64
 
0.1%
1.34 306
 
0.7%
1.35 12
 
< 0.1%
1.78 1836
 
4.2%
1.79 4807
11.0%
1.8 2
 
< 0.1%
2.23 176
 
0.4%
2.24 10
 
< 0.1%
ValueCountFrequency (%)
585.6 1
< 0.1%
581.58 1
< 0.1%
577.56 1
< 0.1%
573.54 1
< 0.1%
570.41 1
< 0.1%
565.49 1
< 0.1%
559.68 1
< 0.1%
552.53 1
< 0.1%
543.59 1
< 0.1%
534.65 1
< 0.1%

Is
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.052733662
Minimum0
Maximum27
Zeros43456
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2023-12-07T06:02:39.912721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.76037474
Coefficient of variation (CV)14.419153
Kurtosis449.08245
Mean0.052733662
Median Absolute Deviation (MAD)0
Skewness19.483592
Sum2311
Variance0.57816974
MonotonicityNot monotonic
2023-12-07T06:02:40.019823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 43456
99.2%
1 66
 
0.2%
2 46
 
0.1%
3 37
 
0.1%
4 31
 
0.1%
5 27
 
0.1%
6 25
 
0.1%
7 21
 
< 0.1%
8 20
 
< 0.1%
9 15
 
< 0.1%
Other values (18) 80
 
0.2%
ValueCountFrequency (%)
0 43456
99.2%
1 66
 
0.2%
2 46
 
0.1%
3 37
 
0.1%
4 31
 
0.1%
5 27
 
0.1%
6 25
 
0.1%
7 21
 
< 0.1%
8 20
 
< 0.1%
9 15
 
< 0.1%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 2
< 0.1%
22 2
< 0.1%
21 2
< 0.1%
20 3
< 0.1%
19 4
< 0.1%
18 4
< 0.1%

Ir
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19491603
Minimum0
Maximum36
Zeros42016
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2023-12-07T06:02:40.127106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4158667
Coefficient of variation (CV)7.263983
Kurtosis174.42485
Mean0.19491603
Median Absolute Deviation (MAD)0
Skewness11.662153
Sum8542
Variance2.0046785
MonotonicityNot monotonic
2023-12-07T06:02:40.244194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 42016
95.9%
1 529
 
1.2%
2 316
 
0.7%
3 214
 
0.5%
4 136
 
0.3%
5 113
 
0.3%
6 88
 
0.2%
7 74
 
0.2%
8 55
 
0.1%
9 45
 
0.1%
Other values (27) 238
 
0.5%
ValueCountFrequency (%)
0 42016
95.9%
1 529
 
1.2%
2 316
 
0.7%
3 214
 
0.5%
4 136
 
0.3%
5 113
 
0.3%
6 88
 
0.2%
7 74
 
0.2%
8 55
 
0.1%
9 45
 
0.1%
ValueCountFrequency (%)
36 1
< 0.1%
35 1
< 0.1%
34 1
< 0.1%
33 1
< 0.1%
32 2
< 0.1%
31 2
< 0.1%
30 2
< 0.1%
29 2
< 0.1%
28 2
< 0.1%
27 2
< 0.1%

Interactions

2023-12-07T06:02:35.727939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:25.252172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.223409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.180606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.408955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.518822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.527163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.575642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.621230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:33.781090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.777010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.811964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:25.337741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.303463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.264792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.497527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.607853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.621700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.660212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.722752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:33.868675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.858364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.903092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:25.424318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.387008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.351389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.584365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.697401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.713002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.751765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.809111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:33.970128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.941928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.993680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:25.513342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.476574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.444196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.673543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.795939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.811574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.858287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.898677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.066685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.031737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:36.078240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:25.592335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.559158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.539749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.756119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.885472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.900125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.957361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.979229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.152229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.114765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:36.166816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:25.678962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.644724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.774857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.864673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.975559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.998659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.050289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:33.065825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.236828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.199533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:36.260956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:25.769164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.737294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.872444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.971867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.076101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.105935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.150495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:33.159061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.329932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.288599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:36.354748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:25.855518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.833864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.982512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.083009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.170625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.204463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.246462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:33.255308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.423143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.376158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:36.440911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:25.957495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.917468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.090627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.199486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.258794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.296989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.337175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:33.349824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.509260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.459704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:36.534600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.052003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.007485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.210881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.305023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.343027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.395536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.428710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:33.441431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.599784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.548042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:36.620209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:26.133569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:27.091043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:28.307410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:29.414271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:30.431602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:31.483059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:32.516002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:33.683513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:34.684340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T06:02:35.630989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-07T06:02:36.751084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-07T06:02:36.940430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Noyearmonthdayhourpm2.5DEWPTEMPPREScbwdIwsIsIr
012010110NaN-21-11.01021.0NW1.7900
122010111NaN-21-12.01020.0NW4.9200
232010112NaN-21-11.01019.0NW6.7100
342010113NaN-21-14.01019.0NW9.8400
452010114NaN-20-12.01018.0NW12.9700
562010115NaN-19-10.01017.0NW16.1000
672010116NaN-19-9.01017.0NW19.2300
782010117NaN-19-9.01017.0NW21.0200
892010118NaN-19-9.01017.0NW24.1500
9102010119NaN-20-8.01017.0NW27.2800
Noyearmonthdayhourpm2.5DEWPTEMPPREScbwdIwsIsIr
438144381520141231149.0-271.01032.0NW196.2100
4381543816201412311511.0-261.01032.0NW205.1500
438164381720141231168.0-230.01032.0NW214.0900
438174381820141231179.0-22-1.01033.0NW221.2400
4381843819201412311810.0-22-2.01033.0NW226.1600
438194382020141231198.0-23-2.01034.0NW231.9700
4382043821201412312010.0-22-3.01034.0NW237.7800
4382143822201412312110.0-22-3.01034.0NW242.7000
438224382320141231228.0-22-4.01034.0NW246.7200
4382343824201412312312.0-21-3.01034.0NW249.8500